bink.ipynb

{
 "metadata": {
  "name": "bink"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python sandbox/bin-reads-by-abundance.py data/stamps-reads.fa.gz "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 0\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "PING\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 25000\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 50000\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "PING\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "PING\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 75000\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 100000\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 125000\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 150000\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "... 175000\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "199999 7359 9413 175 183053\r\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python scripts/abundance-dist-single.py -s -k 20 -x 1e8 stamps-reads.fa.gz.bink.2 bink2.hist\n",
      "!python scripts/abundance-dist-single.py -s -k 20 -x 1e8 stamps-reads.fa.gz.bink.1 bink1.hist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 1e+08 \t(-x)\r\n",
        "\r\n",
        "Estimated memory usage is 4e+08 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "building tracking ht\r\n",
        "K: 20\r\n",
        "HT sizes: [100000007, 100000037, 100000039, 100000049]\r\n",
        "outputting to bink2.hist\r\n",
        "consuming input, round 1 -- stamps-reads.fa.gz.bink.2\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "preparing hist from stamps-reads.fa.gz.bink.2...\r\n",
        "consuming input, round 2 -- stamps-reads.fa.gz.bink.2\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 1e+08 \t(-x)\r\n",
        "\r\n",
        "Estimated memory usage is 4e+08 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "building tracking ht\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [100000007, 100000037, 100000039, 100000049]\r\n",
        "outputting to bink1.hist\r\n",
        "consuming input, round 1 -- stamps-reads.fa.gz.bink.1\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "preparing hist from stamps-reads.fa.gz.bink.1...\r\n",
        "consuming input, round 2 -- stamps-reads.fa.gz.bink.1\r\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw = numpy.loadtxt('raw-reads.hist')\n",
      "bink = numpy.loadtxt('bink-reads.hist')\n",
      "bink1 = numpy.loadtxt('bink1.hist')\n",
      "bink2 = numpy.loadtxt('bink2.hist')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(raw[:,0], raw[:,1])\n",
      "plot(bink[:,0], bink[:,1])\n",
      "axis(ymax=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "(0.0, 3500.0, 0.0, 200)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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tt6qPU1PV5/Pne+9Xnz8tACFmzuy7/0c/Uh8XFASoP3yXazL53q/9jB7dd3th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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c163a90>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(raw[:,0], raw[:,1])\n",
      "plot(bink[:,0], bink[:,1])\n",
      "axis(ymax=200, xmax=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "(0.0, 200, 0.0, 200)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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4QjOZTNqECRO09PR0LT09Xfvoo4/c+v506944Qggh1CQL\ntEII0Q9I2AshRD8gYS+EEP2AhL0QQvQDEvZCCNEPSNgLIUQ/8P8BmXwZtYn/KboAAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109e46850>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(bink1[:,0], bink1[:,1])\n",
      "plot(bink2[:,0], bink2[:,1])\n",
      "axis(ymax=800)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "(0.0, 120.0, 0.0, 800)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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kn5l5+ft+XfuRmpeq6TmEaChf7f2K+5ffT9JDSUT0jADguP04QxOHMqzHMJaM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\nkLAXQohmQMJeCCGagf8H8qA+GMXDlZkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c13c2d0>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python scripts/load-into-counting.py -x 1e7 -k 20 bink1.kh stamps-reads.fa.gz.bink.1\n",
      "!python scripts/abundance-dist.py -s bink1.kh a.fa bink1.x.a.hist\n",
      "!python scripts/abundance-dist.py -s bink1.kh b.fa bink1.x.b.hist\n",
      "\n",
      "!python scripts/load-into-counting.py -x 1e7 -k 20 bink2.kh stamps-reads.fa.gz.bink.2\n",
      "!python scripts/abundance-dist.py -s bink2.kh a.fa bink2.x.a.hist\n",
      "!python scripts/abundance-dist.py -s bink2.kh b.fa bink2.x.b.hist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 1e+07 \t(-x)\r\n",
        "\r\n",
        "Estimated memory usage is 4e+07 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "Saving hashtable to bink1.kh\r\n",
        "Loading kmers from sequences in ['stamps-reads.fa.gz.bink.1']\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "consuming input stamps-reads.fa.gz.bink.1\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "saving bink1.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fp rate estimated to be 0.000\r\n",
        "DONE.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hashtable from bink1.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [10000019, 10000079, 10000103, 10000121]\r\n",
        "outputting to bink1.x.a.hist\r\n",
        "** squashing existing file bink1.x.a.hist\r\n",
        "preparing hist...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hashtable from bink1.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [10000019, 10000079, 10000103, 10000121]\r\n",
        "outputting to bink1.x.b.hist\r\n",
        "** squashing existing file bink1.x.b.hist\r\n",
        "preparing hist...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 1e+07 \t(-x)\r\n",
        "\r\n",
        "Estimated memory usage is 4e+07 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "Saving hashtable to bink2.kh\r\n",
        "Loading kmers from sequences in ['stamps-reads.fa.gz.bink.2']\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "consuming input stamps-reads.fa.gz.bink.2\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "saving bink2.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fp rate estimated to be 0.000\r\n",
        "DONE.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hashtable from bink2.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [10000019, 10000079, 10000103, 10000121]\r\n",
        "outputting to bink2.x.a.hist\r\n",
        "** squashing existing file bink2.x.a.hist\r\n",
        "preparing hist...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hashtable from bink2.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [10000019, 10000079, 10000103, 10000121]\r\n",
        "outputting to bink2.x.b.hist\r\n",
        "** squashing existing file bink2.x.b.hist\r\n",
        "preparing hist...\r\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bink1xa = numpy.loadtxt('bink1.x.a.hist')\n",
      "bink1xb = numpy.loadtxt('bink1.x.b.hist')\n",
      "bink2xa = numpy.loadtxt('bink2.x.a.hist')\n",
      "bink2xb = numpy.loadtxt('bink2.x.b.hist')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(bink1xa[:,0], bink1xa[:,1], label='bink1 x a')\n",
      "plot(bink1xb[:,0], bink1xb[:,1], label='bink1 x b')\n",
      "plot(bink2xa[:,0], bink2xa[:,1], label='bink2 x a')\n",
      "plot(bink2xb[:,0], bink2xb[:,1], label='bink2 x b')\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<matplotlib.legend.Legend at 0x10c13cfd0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Qc2bv4iIye8GK1dbCf/5z6fO7dkFWVvfOkZgIK1bot109UNNYQ3VjNYOcBxns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\nj5RZnNnm2BVbV0izPpklabQaafrH06V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vL5MnTyY6Otpq7k/ncveTkpJCWFhYy3EhISEW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\niHzgD4PrMzaSsLNiAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c0c7a90>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "` "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}